U-net is an image segmentation technique developed primarily for image segmentation
tasks. These traits provide U-net with a high utility within the medical imaging community …

Retinal fundus images play a crucial role in the early detection of eye problems, aiding in
timely diagnosis and treatment to prevent vision loss or blindness. With advancements in …

Abstract The Segment Anything Model (SAM) is the first foundation model for general image
segmentation. It has achieved impressive results on various natural image segmentation …

The implementation of clinical-decision support algorithms for medical imaging faces
challenges with reliability and interpretability. Here, we establish a diagnostic tool based on …

Glaucoma is one of the ophthalmological diseases that frequently causes loss of vision in
today's society. Previous studies assess which anatomical parameters of the optic nerve can …

Foundation vision-language models are currently transforming computer vision, and are on
the rise in medical imaging fueled by their very promising generalization capabilities …

We developed a contour detection based image processing algorithm based on Mamdani
(Type-2) fuzzy rules for detection of blood vessels in retinal fundus images. The method …

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting
the disease in time is important. Current tests using intraocular pressure (IOP) are not …

The development of an automatic telemedicine system for computer-aided screening and
grading of diabetic retinopathy depends on reliable detection of retinal lesions in fundus …

Diabetes mellitus may cause alterations in the retinal microvasculature leading to diabetic
retinopathy. Unchecked, advanced diabetic retinopathy may lead to blindness. It can be …